Penerapan Artifical Intellegence Radial Basis Function (RBF) dalam memproyeksikan Data Indeks Pembangunan Manusia (IPM) di NTB Menggunakan Software Matlab

Rani Astikayanti (Universitas Islam Negeri (UIN) Mataram)
Siti Lida Haspiani (Universitas Islam Negeri (UIN) Mataram)
Wiwin Diyana Safitri (Universitas Islam Negeri (UIN) Mataram)
Martin Ruhma Indayani (Universitas Islam Negeri (UIN) Mataram)
Lila Wahyuni (Universitas Islam Negeri (UIN) Mataram)

Jurnal Riset Teknologi dan Inovasi Pedidikan (JARTIKA)
Vol. 2 No. 2 (2019): Juni

In determining the Human Development Index in the coming year can use forecasting. Where in doing the forecasting there are several methods, one of which is the Radial Basis Function (RBF). This RBF method is part of Artificial Intelligence with Network type used in this study, namely the fewer network. To do forecasting with this method, it takes two types of data, namely input data and target data to produce output data and data errors, where the determination also depends on the value of the performance goal and spread constant. In this study, the data used is the NTB Human Development Index (HDI) data for 2010-2018 with the forecast results obtained not far from the data values from the previous year. Such as, West Lombok as much as 66.7917 with an error of 0.26835; Central Lombok 65.0776 with error -0.0075804; East Lombok 65.1453 with an error of 0.054664; Sumbawa 66.3912 with error -0.12115; Dompu 66.9601 with error -0.15005; Bima 65.6131 with error -0.043123; Sumbawa Barat 70.41 with an error 6.0736e-07; North Lombok 64.1411 with error -0.0011034; Mataram 78.73 with error 0; and Bima 74.85 with error -2.8422e-14.